Automated Detection of Helminth Eggs in Stool Samples Using Convolutional Neural Networks

Schistosomiasis, trichuriasis, and ascariasis are few of the many neglected tropical diseases that still affect populations in poor countries. These diseases cause a variety of symptoms such as abdominal pain, may lead to complications, and may even result in death in severe schistosomiasis cases. To complement the efforts of governments and health organizations in mitigating the morbidity and transmission of neglected tropical diseases, several applications utilizing machine learning techniques have been developed in recent years to automate the detection of parasites in microscopy samples. In this paper, we explore the use of YOLO, a convolutional neural network framework, in the detection of helminth eggs in stool samples. We collected and labelled a dataset with varying imaging conditions due to different staining conditions and acquisition by smartphone cameras with different parameters. We demonstrate that the approach works well despite this variance in imaging conditions in the dataset, achieving high sensitivity in the detection of helminth eggs and high accuracy in the identification of egg species. The trained model operates in real-time, making it suitable for automated diagnosis and real-time annotation.